تقرير
AlanaVLM: A Multimodal Embodied AI Foundation Model for Egocentric Video Understanding
| العنوان: | AlanaVLM: A Multimodal Embodied AI Foundation Model for Egocentric Video Understanding |
|---|---|
| المؤلفون: | Suglia, Alessandro, Greco, Claudio, Baker, Katie, Part, Jose L., Papaioannou, Ioannis, Eshghi, Arash, Konstas, Ioannis, Lemon, Oliver |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Computation and Language |
| الوصف: | AI personal assistants deployed via robots or wearables require embodied understanding to collaborate with humans effectively. However, current Vision-Language Models (VLMs) primarily focus on third-person view videos, neglecting the richness of egocentric perceptual experience. To address this gap, we propose three key contributions. First, we introduce the Egocentric Video Understanding Dataset (EVUD) for training VLMs on video captioning and question answering tasks specific to egocentric videos. Second, we present AlanaVLM, a 7B parameter VLM trained using parameter-efficient methods on EVUD. Finally, we evaluate AlanaVLM's capabilities on OpenEQA, a challenging benchmark for embodied video question answering. Our model achieves state-of-the-art performance, outperforming open-source models including strong Socratic models using GPT-4 as a planner by 3.6%. Additionally, we outperform Claude 3 and Gemini Pro Vision 1.0 and showcase competitive results compared to Gemini Pro 1.5 and GPT-4V, even surpassing the latter in spatial reasoning. This research paves the way for building efficient VLMs that can be deployed in robots or wearables, leveraging embodied video understanding to collaborate seamlessly with humans in everyday tasks, contributing to the next generation of Embodied AI. Comment: Code available https://github.com/alanaai/EVUD |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2406.13807 |
| رقم الأكسشن: | edsarx.2406.13807 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |